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Adaptive radiation has become a central topic in modern evolutionary biology. Adaptive radiations are generally thought to involve rapid diversification of species (i.e. high rates of speciation minus extinction) accompanied by the evolution of ecologically relevant phenotypes (Schluter, 2000; Gillespie et al., 2020; Moen et al., 2021). One reason that adaptive radiations are of such great interest is that they are thought to underlie much of the species diversity and phenotypic diversity of life (Simpson, 1953; Schluter, 2000; Glor, 2010; Givnish, 2015). After all, if the idea of adaptive radiation applied to only a few exceptional clades with limited species diversity (e.g. African rift-lake cichlids, Caribbean anoles, Galápagos finches, sticklebacks), then it is unclear why adaptive radiation should be a broadly important topic in the field. However, with the exception of a recent study in frogs (Morinaga et al., 2023), no studies have explicitly quantified how much of Earth’s overall species richness is contained in clades resembling adaptive radiations.

The study in frogs (Morinaga et al., 2023) developed a framework for addressing how much species diversity and phenotypic diversity is explained by clades with different dynamics of diversification and phenotypic evolution (following Moen et al., 2021). Thus, they classified clades as resembling adaptive radiations (those with rapid phenotypic change and rapid species diversification), non-adaptive radiations (those with rapid species diversification but unexceptional rates of phenotypic change), adaptive non-radiations (with rapid phenotypic change but unexceptional diversification rates), and non-adaptive, non-radiations (with relatively slow rates of phenotypic change and species diversification). They found that in frogs ~75% of both species richness and phenotypic diversity was contained in clades resembling adaptive radiations, with above-average rates of diversification and phenotypic change. Here and throughout, we refer to diversification rates as the rate of speciation minus the rate of extinction, or the rate of species accumulation over time. For brevity, we use “diversification rate” instead of “net diversification rate”.

Here, we apply this general framework (Moen et al., 2021; Morinaga et al., 2023) to ask: how much of life’s current species diversity evolved from relatively rapid radiations? It would be very difficult to estimate comparable species-level rates of phenotypic change for hundreds of species in every major clade across life. Therefore, without phenotypic data and rates, one cannot distinguish adaptive radiations from non-adaptive radiations, adaptive non-radiations, and non-adaptive non-radiations. Nevertheless, we can quantify how much of life’s species richness is contained within relatively rapid radiations (i.e. clades with high diversification rates) as opposed to non-radiations (i.e. clades with lower diversification rates).

Some previous analyses have found that variation in species richness of named clades of comparable rank (e.g. families, phyla) is strongly related to the diversification rates of these clades (Scholl and Wiens, 2016). However, this relationship alone says nothing about what proportion of species belong to rapidly diversifying clades as opposed to clades with more moderate diversification rates. For example, in both of the hypothetical examples in Figure 1 , diversification rates and species richness are very strongly related (Example A: r ² = 0.98, P=0.002; Example B: r ² = 0.93, P=0.008; details in Figure 1 , Supplementary Table S1 ). But in Example A, the most rapidly diversifying clade contains only 30% of the group’s species richness, whereas in Example B, the most rapidly diversifying clade contains 80%. Overall, the contribution of rapid radiations to life’s species diversity remains unknown, and is not addressed by the relationship between diversification rates and species richness.

Two factors now facilitate addressing this question. First, several recent studies have analyzed patterns of diversification rates and richness across all of life, and among major clades of land plants, animals, insects, and vertebrates (see Methods for details). Thus, many of the building blocks needed to address this question are now available. Second, recent studies have also shown that the clade-based estimator of diversification rates used in these studies (Magallón and Sanderson, 2001) yields estimates that are strongly related to true diversification rates in simulations (r ²~0.7), including simulations in which speciation and extinction rates vary within clades over time and among subclades (Meyer and Wiens, 2018; Meyer et al., 2018). Empirical analyses of randomly selected clades across life (Yu and Wiens, 2024; see also Supplementary Dataset S1, Supplementary Table S2 ) show that these clade-based estimates (Magallón and Sanderson, 2001) generally are strongly related (r ²~0.8) to estimates from a new Bayesian species-based method (ClaDS; Maliet et al., 2019; Maliet and Morlon, 2022). These two lines of evidence suggest that these clade-based estimates are accurate and a reasonable proxy for estimates from this Bayesian species-level method. These clade-based estimators can estimate diversification rates given only the age and species richness of each clade (Magallón and Sanderson, 2001). Therefore, a well-sampled, fully-resolved, time-calibrated, species-level phylogeny is not necessary to estimate diversification rates within each clade using this clade-based approach, unlike ClaDS and many other estimators based on species-level branch lengths. By using this clade-based estimator, we can conduct analyses across all of life and within many major groups that lack a comprehensive species-level phylogeny (i.e. most of them).

In this study we quantify how much of life’s species richness is contained within relatively rapid radiations. We do this across living organisms and within some of the most species-rich groups, including land plants, animals, insects, and vertebrates. For each of these five groups, we compile data on diversification rates and species richness for named clades. We then determine what proportion of the richness of each group is contained within clades with above-average diversification rates (following Morinaga et al., 2023), and those with diversification rates in the upper 75th, 90th, and 95th percentiles. These new analyses allow us to address the long-standing question of whether most of the diversity of life is contained within rapid radiations.

Among known species, we found that clades with above-average diversification rates generally encompassed >70% of the species in each major clade ( Table 1 ). Furthermore, when considering higher-level clades (kingdoms, phyla), those clades with exceptional diversification rates (upper 90th percentile, 10% of the clades) generally included >85% of the species in each group ( Figures 2 , 3 ; Table 1 ). However, the clades with the fastest diversification rates (upper 95th percentile) often contained only a minority of species. When clades were subdivided into lower ranking taxa (e.g. families), richness was more evenly distributed among clades ( Table 1 ), but the majority of species in each group generally belonged to those clades in the upper 75th percentile for diversification rates ( Figure 3 , Table 1 ). We describe these results in detail for each taxonomic group below.

Across 17 kingdom-level clades spanning all of life ( Figure 2 ; Table 1 ; Supplementary Dataset S2), we found that the six clades with above-average diversification rates encompassed 99% of known species, and the two clades in the upper 90th percentile (animals and land plants) included 90% of known species ( Figure 3 ). However, the most rapidly diversifying of these clades (land plants) included only 17% of known species. Among these 17 clades, land plants are much younger than animals but have higher diversification rates and less richness. When these clades were subdivided into families (Supplementary Dataset S3), the majority of species (58%) were in clades in the upper 75th percentile for diversification rates, but with only a minority of species among those clades in the upper 90th and 95th percentiles ( Table 1 ).

Among the 10 phyla of land plants ( Figure 2 ; Table 1 ; Supplementary Dataset S4), 98% of species belonged to clades with above-average diversification rates, and 91% belonged to a single clade (angiosperms, Magnoliophyta) with the fastest diversification rate (95th percentile). Among 140 land plant orders ( Figure 3 ; Table 1 ; Supplementary Dataset S5), 92–97% of species belonged to orders with above-average diversification rates (ranges refer to different trees), whereas 69–84% belonged to those in the upper 75th percentile and 46–59% in the upper 90th percentile. Among the 678 land plant families ( Table 1 ; Supplementary Dataset S6), 92–94% of species were in families with above-average diversification rates, 74–83% were in those with high diversification rates (upper 75th percentile; Figure 2 ), and 44–55% were in those with exceptional diversification rates (upper 90th percentile).

Among 28 phyla of animals ( Figure 2 ; Table 1 ; Supplementary Datasets S7–S9), 97–98% of species belonged to phyla with high diversification rates (75th percentile) and 86–89% were in phyla with exceptional diversification rates (90th percentile; Figure 3 ). These patterns are largely explained by the relatively rapid diversification and numerical dominance of arthropods ( Figure 2 ). Among 1,710 animal families ( Table 1 ; Supplementary Dataset S10), 75% of included species belonged to clades with above-average diversification rates and 53% belonged to those with high diversification rates (upper 75th percentile; Figure 3 ). Only a minority of species (16–27%) belonged to families with exceptional diversification rates (upper 90th–95th percentiles).

Among 31 insect (hexapod) orders ( Table 1 ; Supplementary Dataset S11), 96% of species belonged to those with above-average diversification rates, and 71% belonged to those with high diversification rates (75th percentile; Figure 4 ). Only a minority of species belonged to orders with exceptional diversification rates (90th and 95th percentile). Among 870 insect families ( Table 1 ; Supplementary Dataset S12), 72% of species belonged to families with above-average diversification rates, and 53% belonged to families with high diversification rates (75th percentile).

Among the 12 major clades of vertebrates (classes or equivalent; Table 1 ; Supplementary Dataset S13), 98% of species belonged to clades with above-average diversification rates ( Figure 4 ), but only a minority of species (16–39%) belonged to clades with high and exceptional diversification rates (75th to 95th percentiles; Figure 4 ). This pattern occurred because almost half of vertebrate species belonged to the clade Actinopterygia (ray-finned fishes), which has diversification rates that are merely above-average and not high or exceptional (Supplementary Dataset S13). For 144 vertebrate orders (Supplementary Dataset S14), 79% of species were in orders with above-average rates, and 40–64% belonged to orders with high and exceptional rates (75th to 95th percentiles). Among 778 vertebrate families (Supplementary Dataset S15), 81% of species richness was in families with above-average rates, 53% was in families with high rates (75th percentile), and a minority of species belonged to families with exceptional rates (90th and 95th percentiles).

These baseline results utilized the MS estimator with ϵ=0.5. Results were similar using ϵ=0.9 ( Supplementary Table S3 ). The most notable difference was among animal phyla: using ϵ=0.9, most species richness (83%) was in the clade with an exceptionally fast diversification rate (Arthropoda; upper 95th percentile), but using ϵ=0.5 the clade with the fastest rate (Nematoda) contained relatively little known richness (2%).

We then revisited the higher-level analyses with new estimates of diversification based on new estimates of species richness ( Table 2 ; Supplementary Appendix S1 ; animal phyla: Supplementary Datasets S16–S18; insect orders: Supplementary Dataset S19), and new estimates of richness combined with new estimates of clade ages (vertebrate classes: Supplementary Dataset S20; land plant phyla: Supplementary Dataset S21). The results were very similar, in terms of how much richness was contained within rapidly diversifying clades. The only substantive difference was (again) that in some analyses of animal phyla with revised richness data, the clades with exceptional rates (95th percentile) included 78% of the species (for Trees 1 and 2), instead of 2%.

We also examined the impact of using projected species numbers across life ( Table 3 ; Supplementary Dataset S22), instead of only described species. We found that only 22–23% of projected species belonged to clades with above-average diversification rates. This disparate pattern arose because in these projections ~77% of all species are bacteria, which have below-average diversification rates, given their ancient age relative to other clades.

Considering only eukaryotes ( Table 3 ; Supplementary Dataset S22), then among the 15 kingdom-level clades, 99.9% of projected species belonged to those with above-average diversification rates and 68.1–99.9% belonged to those with high diversification rates (75th percentile; range represents low and high richness projections). Only a minority of projected species belonged to clades with exceptional diversification rates (90th and 95th percentiles).

Additionally we examined the impact of defining the largest-scale clades based on clade ages rather than taxonomic ranks alone ( Table 4 ). Among kingdoms, we used 14 clades that were each at least 1 billion years old (range=1.08–4.20 billion years; Supplementary Dataset S23) and then 9 clades that were at least 1.5 billion years old (range=1.55–4.20 billion years; Supplementary Dataset S24). Using this approach, almost all species richness (98–99%) was in clades with above-average or high diversification rates (75th percentile). With these 14 clades, 74% of species richness was in clades with exceptional diversification rates (90–95th percentile), but only 18% was when using the 9 clades that were >1.5 billion years old (similar to the original results). Among land plant phyla ( Table 4 ), we used 6 clades that were at least 400 million years (Myr) old (range=459–556 Myr or 431–511 Myr depending on the tree; Supplementary Dataset S25): the results were almost identical to those using 10 phyla (with 91% of richness contained among clades with exceptional diversification rates; 90th–95th percentile). Among animal phyla ( Table 4 ; Supplementary Datasets S26–S27), we used seven clades that were at least 700 Myr old (Tree 1: range=757–1,031 Myr; Tree 2: 703–876). Results were similar to those based on 28 phyla, except that 94% of richness was contained in clades with exceptional diversification rates (90th–95th percentile). In summary, using clades defined based on clade ages gave similar results to the original analyses based on named clades, but with the most rapidly diversifying clades often encompassing more richness.

Rapid radiations are thought to contain most of life’s species diversity, but this idea has not been tested explicitly. Here, we find that most known species belong to a small fraction of higher-level clades with relatively rapid diversification rates. We show this pattern across the major clades of life and among the major clades of plants and animals ( Figures 2 , 3 ; Table 1 ).

These results are not apparent from previous studies nor do they appear to be methodological artifacts. Given previous analyses showing a relationship between diversification rates and species richness across life (Scholl and Wiens, 2016), one might argue that it is inevitable that those clades with more species would have higher diversification rates. But the patterns described here depend on how species are divided among clades with different diversification rates, not the relationship between diversification rates and species richness among clades (see example in Figure 1 ). We show that at the highest taxonomic levels (kingdoms, phyla), these rapidly diversifying clades (upper 90th percentile) contain >85% of all species ( Table 1 ). This pattern is also not obvious because the clades with the very fastest diversification rates (95th percentile) need not contain the majority of species ( Table 1 ). For example, among kingdom-level clades, plants have faster diversification rates than animals, but plants contain only a small minority of known species. These patterns of disparity in richness among clades are also reduced using lower taxonomic ranks (e.g. families). When using lower taxonomic ranks, the large, rapidly diversifying clades are subdivided into smaller clades that must each contain a smaller proportion of the group’s overall species richness. Nevertheless, even when using these lower taxonomic ranks, a minority of rapidly diversifying clades (upper 75th percentile) typically contains the majority of a group’s species richness ( Table 1 ; Figure 2 ). These overall patterns were generally robust to alternative estimates of clade ages, richness, and diversification rates ( Tables 2 , 3 ; Supplementary Table S3 ), and the results for families were similar regardless of whether sampling of families in the group is largely complete or spanned ~67–78% of the group’s species ( Table 1 ). We obtained similar results using ages to define clades, rather than using named ranked taxa ( Table 4 ). Thus, our results are not simply contingent on how humans divide the Tree of Life into named clades (see also Table 1 , showing results from alternative divisions of species into clades). Furthermore, the estimator of diversification rates used here gives estimates that are strongly related to the true rates (in simulations) and to a Bayesian species-based estimator (ClaDS) in empirical analyses. Thus, there is no basis for claiming that this method is problematic. Our goal was not to test the overall relationship between diversification rates and richness, but even if it were, such relationships are neither inevitable nor artifactual (Kozak and Wiens, 2016) nor are they circular (see Methods). Indeed, other empirical studies have suggested that diversification rates often do not underlie richness patterns (McPeek and Brown, 2007; Rabosky et al., 2012; Hedges et al., 2015; Yu and Wiens, 2024), further showing that our results are not obvious. On the other hand, our goal was to empirically test a long-standing idea in the macroevolutionary literature, and so our results (which support that idea) are not shocking from that perspective.

The most important sensitivity of the results related to the inclusion of projected, undescribed bacterial species ( Table 3 ). At present, there are only ~10,000 described species of bacteria (Bánki et al., 2024). Among recent estimates of actual bacterial richness, even lower estimates are in the millions (Louca et al., 2019) and upper estimates are in the billions or trillions (Locey and Lennon, 2016). Because bacteria are very old relative to other major clades, they would still have relatively low overall diversification rates even if they contained billions or trillions of species (see calculations in Scholl and Wiens, 2016). Thus, if actual bacterial richness really is much higher than described richness for other groups, then a clade with low diversification rates would contain the majority of species across life (unlike our other results). Therefore, we caution that our results apply primarily to known species diversity. Nevertheless, if we restrict ourselves to eukaryotes, then the majority of species belong to clades in the upper 75th percentile for diversification rates, even if there are tens of millions of undescribed eukaryotic species. We also acknowledge that these projections of undescribed richness could be incorrect, which is why we treat our analyses of known diversity as the baseline.

Our focus here has been on how much species richness is contained within rapid radiations, but how many of these rapid radiations might be adaptive radiations? There is considerable debate about the definition of adaptive radiation (e.g. Glor, 2010; Givnish, 2015; Hernández-Hernández, 2019; Gillespie et al., 2020; Moen et al., 2021). Some standard definitions require relatively rapid speciation within a clade, along with evidence for correlations between phenotypic traits and environments among species and the functional utility of the traits (Schluter, 2000; Gillespie et al., 2020). Many groups analyzed here may meet this broad criterion. Previous analyses have identified specific phenotypic traits and/or key innovations that seem to drive rapid diversification in these groups. Across life, a key trait that seems to explain rapid diversification rates is multicellularity (Chen and Wiens, 2021), which may have paved the way for much of the phenotypic variability (e.g. different cells and tissue types) in plants, animals, and fungi (Carroll, 2001). Within land plants, insect pollination seems to be a key trait that helps explain the rapid diversification of angiosperms relative to other land plant clades (Hernández-Hernández and Wiens, 2020), as suggested by many previous authors (e.g. Raven, 1977; Stebbins, 1981; Niklas, 2016). Insect pollination may drive considerable phenotypic diversification in flowers (Grant, 1949; Harder and Barrett, 2006) and much plant speciation (Grant, 1949; Sargent, 2004; Kay et al., 2006; van der Niet et al., 2014). In animals and vertebrates, terrestriality seems to be an important driver of diversification among major clades (Wiens, 2015a; 2015b; Jezkova and Wiens, 2017), and the invasion of land may represent a key innovation that allowed access to many open ecological niches (May, 1994; Benton, 2001; Vermeij and Grosberg, 2010). Analyses of diversification rates among insect orders suggest that herbivory may represent a key innovation in some insect clades (Wiens et al., 2015), a longstanding idea in evolutionary ecology (Ehrlich and Raven, 1964; Mitter et al., 1988; Farrell, 1998; Mayhew, 2007; Futuyma and Agrawal, 2009). Host shifts may be a particularly important driver of herbivory-related diversification in insects (e.g. Hardy and Otto, 2014; Forbes et al., 2017). In summary, previous studies identified key traits associated with many of these rapid radiations, and so these particular large clades might represent adaptive radiations (relative to comparable clades in these same groups that did not rapidly diversify). Conversely, the absence of these key traits may help why the majority of clades did not rapidly diversify in each of these groups.

On the other hand, these rapidly diversifying clades may not fit every definition of adaptive radiation. Some definitions require exceptional rates of diversification and phenotypic evolution relative to other clades (e.g. Poe et al., 2018; Moen et al., 2021), but this may be too restrictive. Thus, Morinaga et al. (2023) calculated the relative richness of clades that were “adaptive-radiation-like”, having only above-average rates of diversification and phenotypic evolution. In our study, most species belonged to clades with above-average diversification rates, but not necessarily those in the upper 95th percentile ( Table 1 ). Furthermore, to better test for adaptive radiation, future analyses might also incorporate rates of phenotypic evolution. However, it may be challenging to estimate phenotypic rates from species-level phylogenies from many species across life.

Our results cover all of life and several major groups (plants, animals, insects, vertebrates), but the pattern documented here may also occur within smaller groups. For example, many vertebrate groups are numerically dominated by a single relatively recent clade, such as teleosts within actinopterygian fishes, frogs within amphibians, placentals within mammals, neognaths within birds, and colubroids within snakes (trees from Irisarri et al., 2017; richness data from Bánki et al., 2024). Given that these clades are relatively recent and have high richness, they should have relatively high diversification rates. Similarly, within angiosperms, most species belong to the clade uniting the “core angiosperms” and not the species-poor clades at the base of the angiosperm tree (Magallón et al., 2015; Zuntini et al., 2024).

Similar patterns may also occur in groups that we did not examine in detail here. For example, among the 154,534 currently described species of extant fungi (Bánki et al., 2024), most species (98%) belong to the phyla Ascomycota (98,334 species) and Basidiomycota (52,979 species), and not the other 9 fungal phyla. Phylogenies of fungi show these two phyla as sister taxa that are relatively young among higher-level fungal clades (James et al., 2006; Li et al., 2021). Given their relatively young age and high species richness, these two phyla would almost certainly represent rapid radiations relative to other fungal phyla (higher rates were shown in Scholl and Wiens (2016) but relatively few fungal phyla were included). We acknowledge that we have not confirmed the patterns that we found here within every group across life and at every phylogenetic scale, but we have shown these patterns at the largest phylogenetic scales and within some of the largest groups (e.g. animals, plants). This was our initial question. If this pattern does not hold within most genera or families (for example), it would not overturn our conclusions, which are specifically focused at larger scales.

We emphasize that our study is focused on patterns of extant species richness. We recognize that the relative richness of many clades has changed dramatically over geological time. For example, angiosperms began as a single species, and initially diversified when many other plant clades were already extant and relatively diverse (Knoll, 1986; Niklas, 2016). Moreover, many groups with low diversity today had higher diversity in the past (e.g. coelacanths; Torino et al., 2021), and this might also help explain some of the disparities in richness observed among clades today (especially why some ancient groups are species poor). These observations do not make the patterns described here incorrect. Within a group, the fact that some clades have declined does not invalidate other clades as rapid radiations: diversification includes extinction, and “rapid” is relative and pertains to the timescale of any given comparison. Instead, we caution that these patterns may apply primarily to present day richness patterns. Although it would be interesting to study these patterns in the fossil record for some exemplar groups, it would be difficult to conduct similar analyses across life based on paleontological data.

Many authors have suggested that adaptive radiations are characterized by declining diversification rates over time. We find that much of life (at multiple scales) is numerically dominated by relatively young clades with rapid diversification rates (e.g. animals among kingdoms, arthropods and angiosperms among animal and plant phyla). We favor the idea that adaptive radiations have relatively rapid diversification rates relative to other clades, and not simply declining rates over time within clades. The pattern of declining rates could have many explanations that are unrelated to adaptive radiation (Moen and Morlon, 2014).

In summary, we show here that most of life’s known, extant species richness belongs to relatively rapid radiations. Furthermore, many of these rapid radiations seem to be related to specific phenotypic traits, and thus may qualify as adaptive radiations (under some definitions). Overall, these results support the emphasis in evolutionary biology on finding the drivers of adaptive radiations and other rapid radiations. These results also suggest that much of life’s species diversity might be the products of a nested series of rapid radiations, with each radiation potentially triggered by a different trait (e.g. first multicellularity, then terrestriality, and then herbivory [within insects]). We speculate that such a pattern might help explain the paradox of how rapid radiation is sustained over long timescales (Martin and Richards, 2019). An exciting area for future research will be to see how general these nested radiations are across scales, and to understand how and why certain traits seem to trigger them.